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Multi-objective optimization for connected and automated truck platoon control with improved CACC model

Author

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  • Wang, Kexin
  • Wang, Xiang
  • E, Wenjuan
  • Fan, Mingdi
  • Tong, Jiaxin

Abstract

Connected and Automated Truck Platoon (CATP) refers to a group of trucks traveling closely together with minimal spacing to improve fuel economy and safety. However, challenges arise from instability due to internal platoon factors and external traffic disturbances. This research presents an improved Cooperative Adaptive Cruise Control (CACC) model tailored for CATP to address these challenges. The model is designed to enhance safety, fuel efficiency, and traffic efficacy. The improvements of the proposed model are in two aspects: the optimizing of the time headway strategy and the dynamic parameter adjustments of controller based on multi-objectives. The Dynamic Safety Requirement Time Headway (DSRTH) strategy facilitates the timely detection of the accelerations of the leading vehicles within the platoon, enabling quick driving responses. Additionally, Model Predictive Control (MPC) enables dynamic calibration of Proportional-Derivative (PD) control parameters and issuance of velocity commands. Meanwhile, the integration of a second-order time-delay response model has been implemented to adapt to dynamic changes in commands. A transfer function has been established, and stability has been proven. To evaluate the model performance, simulation analysis was performed using real vehicle trajectories as the CATP following vehicles. The results indicate that the DSRTH strategy outperforms both the Constant Time Headway (CTH) and Variable Time Headway (VTH) strategies, allowing rear vehicles to reach the speed trough earlier, with response speeds improved by 3.1 % and 1.5 %, respectively. Compared to the Intelligent Driver Model (IDM) and CACC models, the improved CACC model achieves a steady state of constant acceleration sooner, with recovery times reduced by 17.7 % and 3.2 %. Additionally, compared to the IDM model, the improved CACC model can save 3.23 % in fuel consumption. Furthermore, sensitivity analysis indicates that as the CATP proportion and platoon size increase, there is a positive impact on traffic flow. However, when the platoon size exceeds 5 vehicles, it shows a negative impact on the stability of other vehicles in the traffic flow besides those in the CATP.

Suggested Citation

  • Wang, Kexin & Wang, Xiang & E, Wenjuan & Fan, Mingdi & Tong, Jiaxin, 2024. "Multi-objective optimization for connected and automated truck platoon control with improved CACC model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
    • Handle: RePEc:eee:phsmap:v:654:y:2024:i:c:s0378437124006459
    DOI: 10.1016/j.physa.2024.130136
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    References listed on IDEAS

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    1. Ahmed Aboelhassan & M. Abdelgeliel & Ezz Eldin Zakzouk & Michael Galea, 2020. "Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications," Energies, MDPI, vol. 13(24), pages 1-22, December.
    2. Li, Xiaopeng, 2022. "Trade-off between safety, mobility and stability in automated vehicle following control: An analytical method," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 1-18.
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    2. Wang, Zhao & Qin, Yanyan, 2025. "A Nash optimal distributed model predictive control for heavy-duty truck platoon to enhance operation and fuel-saving under freeway cut-in conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 670(C).

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